@inproceedings{qiu-etal-2025-measuring,
title = "Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing {LLM} Personalization",
author = "Qiu, Yilun and
Zhao, Xiaoyan and
Zhang, Yang and
Bai, Yimeng and
Wang, Wenjie and
Cheng, Hong and
Feng, Fuli and
Chua, Tat-Seng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1095/",
doi = "10.18653/v1/2025.findings-acl.1095",
pages = "21258--21277",
ISBN = "979-8-89176-256-5",
abstract = "Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personalization, distilling key preference information from an individual{'}s historical data as instructional preference context to customize LLM generation has emerged as a promising direction. However, these methods face a fundamental limitation by overlooking the inter-user comparative analysis, which is essential for identifying the inter-user differences that truly shape preferences. To address this limitation, we propose Difference-aware Personalization Learning (DPL), a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization. DPL strategically selects representative users for comparison and establishes a structured standard to extract meaningful, task-relevant differences for customizing LLM generation. Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization. We release our code at \url{https://github.com/SnowCharmQ/DPL}."
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<abstract>Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personalization, distilling key preference information from an individual’s historical data as instructional preference context to customize LLM generation has emerged as a promising direction. However, these methods face a fundamental limitation by overlooking the inter-user comparative analysis, which is essential for identifying the inter-user differences that truly shape preferences. To address this limitation, we propose Difference-aware Personalization Learning (DPL), a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization. DPL strategically selects representative users for comparison and establishes a structured standard to extract meaningful, task-relevant differences for customizing LLM generation. Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization. We release our code at https://github.com/SnowCharmQ/DPL.</abstract>
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%0 Conference Proceedings
%T Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization
%A Qiu, Yilun
%A Zhao, Xiaoyan
%A Zhang, Yang
%A Bai, Yimeng
%A Wang, Wenjie
%A Cheng, Hong
%A Feng, Fuli
%A Chua, Tat-Seng
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F qiu-etal-2025-measuring
%X Personalizing Large Language Models (LLMs) has become a critical step in facilitating their widespread application to enhance individual life experiences. In pursuit of personalization, distilling key preference information from an individual’s historical data as instructional preference context to customize LLM generation has emerged as a promising direction. However, these methods face a fundamental limitation by overlooking the inter-user comparative analysis, which is essential for identifying the inter-user differences that truly shape preferences. To address this limitation, we propose Difference-aware Personalization Learning (DPL), a novel approach that emphasizes extracting inter-user differences to enhance LLM personalization. DPL strategically selects representative users for comparison and establishes a structured standard to extract meaningful, task-relevant differences for customizing LLM generation. Extensive experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization. We release our code at https://github.com/SnowCharmQ/DPL.
%R 10.18653/v1/2025.findings-acl.1095
%U https://aclanthology.org/2025.findings-acl.1095/
%U https://doi.org/10.18653/v1/2025.findings-acl.1095
%P 21258-21277
Markdown (Informal)
[Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization](https://aclanthology.org/2025.findings-acl.1095/) (Qiu et al., Findings 2025)
ACL
- Yilun Qiu, Xiaoyan Zhao, Yang Zhang, Yimeng Bai, Wenjie Wang, Hong Cheng, Fuli Feng, and Tat-Seng Chua. 2025. Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization. In Findings of the Association for Computational Linguistics: ACL 2025, pages 21258–21277, Vienna, Austria. Association for Computational Linguistics.